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Aneel Advani

Publications and source records attributed to Aneel Advani.

5 recordsLinked to original sources

Improving adherence to guidelines for hypertension drug prescribing: cluster-randomized controlled trial of general versus patient-specific recommendations.

OBJECTIVE: To determine whether an intervention focusing clinician attention on drug choice for hypertension treatment improves concordance between drug regimens and guidelines. STUDY DESIGN: Cluster-randomized controlled trial comparing an individualized intervention with a general guideline implementation in geographically diverse primary care clinics of a university-affiliated Department of Veterans Affairs healthcare system. METHODS: Participants were 36 attending physicians and nurse practitioners (16 in the general group and 20 in the individualized group), with findings based on 4500 hypertensive patients. A general guideline implementation for all clinicians, including education about guideline-based drug recommendations and goals for adequacy of blood pressure control, was compared with addition of a printed individualized advisory sent to clinicians at each patient visit, indicating whether or not the patient's antihypertensive drug regimen was guideline concordant. We measured change from baseline to end point in the proportion of clinicians' patients whose drug therapy was guideline concordant. RESULTS: The individualized intervention resulted in an improvement in guideline concordance more than twice that observed for the general intervention (10.9% vs 3.8%, t = 2.796, P = .008). Bootstrap analysis showed that being in the individualized group increased the odds of concordance 1.5-fold (P = .025). The proportion of patients with adequate blood pressure control increased within each study group; however, the difference between groups was not significant. CONCLUSION: An individualized advisory regarding drug therapy for hypertension given to the clinician at each patient visit was more effective in changing clinician prescribing behavior than implementation of a general guideline.

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An intelligent case-adjustment algorithm for the automated design of population-based quality auditing protocols.

We develop a method and algorithm for deciding the optimal approach to creating quality-auditing protocols for guideline-based clinical performance measures. An important element of the audit protocol design problem is deciding which guide-line elements to audit. Specifically, the problem is how and when to aggregate individual patient case-specific guideline elements into population-based quality measures. The key statistical issue involved is the trade-off between increased reliability with more general population-based quality measures versus increased validity from individually case-adjusted but more restricted measures done at a greater audit cost. Our intelligent algorithm for auditing protocol design is based on hierarchically modeling incrementally case-adjusted quality constraints. We select quality constraints to measure using an optimization criterion based on statistical generalizability coefficients. We present results of the approach from a deployed decision support system for a hypertension guideline.

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Developing quality indicators and auditing protocols from formal guideline models: knowledge representation and transformations.

Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a model representation and algorithm for deriving structured quality indicators and auditing protocols from formalized specifications of guidelines used in decision support systems. We apply the model and algorithm to the assessment of physician concordance with a guideline knowledge model for hypertension used in a decision-support system. The properties of our solution include the ability to derive automatically context-specific and case-mix-adjusted quality indicators that can model global or local levels of detail about the guideline parameterized by defining the reliability of each indicator or element of the guideline.

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A framework for evidence-adaptive quality assessment that unifies guideline-based and performance-indicator approaches.

Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a unified model representation and algorithm for evidence-adaptive quality assessment scoring that can: (1) use both complex case-specific guidelines and single-step population-wide performance-indicators as quality measures; (2) score adherence consistently with quantitative population-based medical utilities of the quality measures where available; and (3) give worst-case and best-case scores for variations based on (a) uncertain knowledge of the best practice, (b) guideline customization to an individual patient or particular population, (c) physician practice style variation, or (d) imperfect reliability of the quality measure. Our solution uses fuzzy measure-theoretic scoring to handle the uncertain knowledge about best-practices and the ambiguity from practice variation. We show results of applying our method to retrospective data from a guideline project to improve the quality of hypertension care.

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